Advanced Lane Finding
In this project, the goal is to write a software pipeline to identify the lane boundaries in a video, but the main output or product we want you to create is a detailed writeup of the project. This notebook is that writeup, becuase I want to create a conherent notebook to demonstrate the informatiion being talked about. Using both the code and results to demonstrate the results.
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
First, I'll compute the camera calibration using chessboard images¶
In [31]:
import numpy as np
import cv2
import pickle
import glob
import matplotlib.pyplot as plt
from ipywidgets import interact, interactive, fixed
from moviepy.editor import VideoFileClip
from IPython.display import HTML
%matplotlib inline
In [2]:
############################## CAMERA CALIBRATION UNDISTORT ###############################
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')
fig, axs = plt.subplots(5,4, figsize=(16, 11))
fig.subplots_adjust(hspace = 0.2, wspace=.001)
axs = axs.ravel()
# Step through the list and search for chessboard corners
for i, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
# this step to refine image points was taken from:
# http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_calib3d/py_calibration/py_calibration.html
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners2)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
axs[i].axis('off')
axs[i].imshow(img)
In [3]:
################################### PERSPECTIVE TRANSFORM ###############################
import pickle
%matplotlib inline
# Test undistortion on an image
img = cv2.imread('camera_cal/calibration3.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
cv2.imwrite('camera_cal/calibration3.jpg',dst)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "camera_cal/wide_dist_pickle.p", "wb" ) )
dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=30)
Out[3]:
In [4]:
################################### PERSPECTIVE TRANSFORM ###############################
import pickle
%matplotlib inline
# Test undistortion on an image
img = cv2.imread('camera_cal/calibration13.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
cv2.imwrite('camera_cal/calibration13.jpg',dst)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "camera_cal/wide_dist_pickle.p", "wb" ) )
dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=30)
Out[4]:
In [22]:
# Choose an image from which to build and demonstrate each step of the pipeline
exampleImg = cv2.imread('./test_images/test5.jpg')
exampleImg = cv2.cvtColor(exampleImg, cv2.COLOR_BGR2RGB)
plt.imshow(exampleImg)
print('...')
In [23]:
# undistort image using camera calibration matrix from above
def undistort(img):
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
print('...')
In [24]:
exampleImg_undistort = undistort(exampleImg)
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(exampleImg)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(exampleImg_undistort)
ax2.set_title('Undistorted Image', fontsize=30)
print('...')
In [ ]:
In [5]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
# Read in an image
image = mpimg.imread('signs_vehicles_xygrad.png')
# Combining Thresholds
def abs_sobel_thresh(image, orient='x', sobel_kernel=3, thresh=(0, 255)):
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
grad_binary = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
grad_binary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
# Return the result
return grad_binary
def mag_thresh(image, sobel_kernel=3, mag_thresh=(0, 255)):
# Calculate gradient magnitude
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
mag_binary = np.zeros_like(gradmag)
mag_binary[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
# Apply threshold
return mag_binary
def dir_threshold(image, sobel_kernel=3, thresh=(0, np.pi/2)):
# Calculate gradient direction
# Grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
dir_binary = np.zeros_like(absgraddir)
dir_binary[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
# Apply threshold
return dir_binary
# Choose a Sobel kernel size
ksize = 15 # Choose a larger odd number to smooth gradient measurements
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh=(0, 255))
grady = abs_sobel_thresh(image, orient='y', sobel_kernel=ksize, thresh=(0, 255))
mag_binary = mag_thresh(image, sobel_kernel=ksize, mag_thresh=(0, 255))
dir_binary = dir_threshold(image, sobel_kernel=ksize, thresh=(0.7, np.pi/2))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
# Run the function
#dir_binary = dir_threshold(image, sobel_kernel=15, thresh=(0.7, 1.3))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dir_binary, cmap='gray')
ax2.set_title('Thresholded Grad. Dir.', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [25]:
def unwarp(img, src, dst):
h,w = img.shape[:2]
# use cv2.getPerspectiveTransform() to get M, the transform matrix, and Minv, the inverse
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
# use cv2.warpPerspective() to warp your image to a top-down view
warped = cv2.warpPerspective(img, M, (w,h), flags=cv2.INTER_LINEAR)
return warped, M, Minv
print('...')
In [26]:
h,w = exampleImg_undistort.shape[:2]
# define source and destination points for transform
src = np.float32([(575,464),
(707,464),
(258,682),
(1049,682)])
dst = np.float32([(450,0),
(w-450,0),
(450,h),
(w-450,h)])
exampleImg_unwarp, M, Minv = unwarp(exampleImg_undistort, src, dst)
# Visualize unwarp
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(exampleImg_undistort)
x = [src[0][0],src[2][0],src[3][0],src[1][0],src[0][0]]
y = [src[0][1],src[2][1],src[3][1],src[1][1],src[0][1]]
ax1.plot(x, y, color='#33cc99', alpha=0.4, linewidth=3, solid_capstyle='round', zorder=2)
ax1.set_ylim([h,0])
ax1.set_xlim([0,w])
ax1.set_title('Undistorted Image', fontsize=30)
ax2.imshow(exampleImg_unwarp)
ax2.set_title('Unwarped Image', fontsize=30)
print('...')
In [27]:
# Visualize multiple color space channels
exampleImg_unwarp_R = exampleImg_unwarp[:,:,0]
exampleImg_unwarp_G = exampleImg_unwarp[:,:,1]
exampleImg_unwarp_B = exampleImg_unwarp[:,:,2]
exampleImg_unwarp_HSV = cv2.cvtColor(exampleImg_unwarp, cv2.COLOR_RGB2HSV)
exampleImg_unwarp_H = exampleImg_unwarp_HSV[:,:,0]
exampleImg_unwarp_S = exampleImg_unwarp_HSV[:,:,1]
exampleImg_unwarp_V = exampleImg_unwarp_HSV[:,:,2]
exampleImg_unwarp_LAB = cv2.cvtColor(exampleImg_unwarp, cv2.COLOR_RGB2Lab)
exampleImg_unwarp_L = exampleImg_unwarp_LAB[:,:,0]
exampleImg_unwarp_A = exampleImg_unwarp_LAB[:,:,1]
exampleImg_unwarp_B2 = exampleImg_unwarp_LAB[:,:,2]
fig, axs = plt.subplots(3,3, figsize=(16, 12))
fig.subplots_adjust(hspace = .2, wspace=.001)
axs = axs.ravel()
axs[0].imshow(exampleImg_unwarp_R, cmap='gray')
axs[0].set_title('RGB R-channel', fontsize=30)
axs[1].imshow(exampleImg_unwarp_G, cmap='gray')
axs[1].set_title('RGB G-Channel', fontsize=30)
axs[2].imshow(exampleImg_unwarp_B, cmap='gray')
axs[2].set_title('RGB B-channel', fontsize=30)
axs[3].imshow(exampleImg_unwarp_H, cmap='gray')
axs[3].set_title('HSV H-Channel', fontsize=30)
axs[4].imshow(exampleImg_unwarp_S, cmap='gray')
axs[4].set_title('HSV S-channel', fontsize=30)
axs[5].imshow(exampleImg_unwarp_V, cmap='gray')
axs[5].set_title('HSV V-Channel', fontsize=30)
axs[6].imshow(exampleImg_unwarp_L, cmap='gray')
axs[6].set_title('LAB L-channel', fontsize=30)
axs[7].imshow(exampleImg_unwarp_A, cmap='gray')
axs[7].set_title('LAB A-Channel', fontsize=30)
axs[8].imshow(exampleImg_unwarp_B2, cmap='gray')
axs[8].set_title('LAB B-Channel', fontsize=30)
print('...')
In [28]:
# Define a function that applies Sobel x or y,
# then takes an absolute value and applies a threshold.
def abs_sobel_thresh(img, orient='x', thresh_min=25, thresh_max=255):
# Apply the following steps to img
# 1) Convert to grayscale === or LAB L channel
gray = (cv2.cvtColor(img, cv2.COLOR_RGB2Lab))[:,:,0]
# 2) Take the derivative in x or y given orient = 'x' or 'y'
sobel = cv2.Sobel(gray, cv2.CV_64F, orient=='x', orient=='y')
# 3) Take the absolute value of the derivative or gradient
abs_sobel = np.absolute(sobel)
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# 6) Return this mask as your binary_output image
binary_output = sxbinary # Remove this line
return binary_output
print('...')
In [32]:
##Visualize Sobel absolute threshold on example image##
def update(min_thresh, max_thresh):
exampleImg_sobelAbs = abs_sobel_thresh(exampleImg_unwarp, 'x', min_thresh, max_thresh)
# Visualize sobel absolute threshold
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(exampleImg_unwarp)
ax1.set_title('Unwarped Image', fontsize=30)
ax2.imshow(exampleImg_sobelAbs, cmap='gray')
ax2.set_title('Sobel Absolute', fontsize=30)
interact(update,
min_thresh=(0,255),
max_thresh=(0,255))
print('...')
In [33]:
##Sobel Magnitude Threshold##
# Define a function that applies Sobel x and y,
# then computes the magnitude of the gradient
# and applies a threshold
def mag_thresh(img, sobel_kernel=25, mag_thresh=(25, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
# 3) Calculate the magnitude
mag_sobel = np.sqrt(np.square(sobelx) + np.square(sobely))
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobel = np.uint8(255*mag_sobel/np.max(mag_sobel))
# 5) Create a binary mask where mag thresholds are met
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= mag_thresh[0]) & (scaled_sobel <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
binary_output = np.copy(sxbinary)
return binary_output
print('...')
In [34]:
def update(kernel_size, min_thresh, max_thresh):
exampleImg_sobelMag = mag_thresh(exampleImg_unwarp, kernel_size, (min_thresh, max_thresh))
# Visualize sobel magnitude threshold
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(exampleImg_unwarp)
ax1.set_title('Unwarped Image', fontsize=30)
ax2.imshow(exampleImg_sobelMag, cmap='gray')
ax2.set_title('Sobel Magnitude', fontsize=30)
interact(update, kernel_size=(1,31,2),
min_thresh=(0,255),
max_thresh=(0,255))
print('...')
In [35]:
# Sobel Direction Threshold
# Define a function that applies Sobel x and y,
# then computes the direction of the gradient
# and applies a threshold.
def dir_thresh(img, sobel_kernel=7, thresh=(0, 0.09)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
grad_dir = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(grad_dir)
binary_output[(grad_dir >= thresh[0]) & (grad_dir <= thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
print('...')
In [36]:
def update(kernel_size, min_thresh, max_thresh):
exampleImg_sobelDir = dir_thresh(exampleImg_unwarp, kernel_size, (min_thresh, max_thresh))
# Visualize sobel direction threshold
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(exampleImg_unwarp)
ax1.set_title('Unwarped Image', fontsize=30)
ax2.imshow(exampleImg_sobelDir, cmap='gray')
ax2.set_title('Sobel Direction', fontsize=30)
interact(update, kernel_size=(1,31,2),
min_thresh=(0,np.pi/2,0.01),
max_thresh=(0,np.pi/2,0.01))
print('...')
In [37]:
#Sobel Magnitude + Direction Threshold
def update(mag_kernel_size, mag_min_thresh, mag_max_thresh, dir_kernel_size, dir_min_thresh, dir_max_thresh):
exampleImg_sobelMag2 = mag_thresh(exampleImg_unwarp, mag_kernel_size, (mag_min_thresh, mag_max_thresh))
exampleImg_sobelDir2 = dir_thresh(exampleImg_unwarp, dir_kernel_size, (dir_min_thresh, dir_max_thresh))
combined = np.zeros_like(exampleImg_sobelMag2)
combined[((exampleImg_sobelMag2 == 1) & (exampleImg_sobelDir2 == 1))] = 1
# Visualize sobel magnitude + direction threshold
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(exampleImg_unwarp)
ax1.set_title('Unwarped Image', fontsize=30)
ax2.imshow(combined, cmap='gray')
ax2.set_title('Sobel Magnitude + Direction', fontsize=30)
interact(update, mag_kernel_size=(1,31,2),
mag_min_thresh=(0,255),
mag_max_thresh=(0,255),
dir_kernel_size=(1,31,2),
dir_min_thresh=(0,np.pi/2,0.01),
dir_max_thresh=(0,np.pi/2,0.01))
print('...')
In [38]:
# HLS S-Channel Threshold
# Define a function that thresholds the S-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)
def hls_sthresh(img, thresh=(125, 255)):
# 1) Convert to HLS color space
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
# 2) Apply a threshold to the S channel
binary_output = np.zeros_like(hls[:,:,2])
binary_output[(hls[:,:,2] > thresh[0]) & (hls[:,:,2] <= thresh[1])] = 1
# 3) Return a binary image of threshold result
return binary_output
print('...')
In [39]:
def update(min_thresh, max_thresh):
exampleImg_SThresh = hls_sthresh(exampleImg_unwarp, (min_thresh, max_thresh))
# Visualize hls s-channel threshold
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(exampleImg_unwarp)
ax1.set_title('Unwarped Image', fontsize=30)
ax2.imshow(exampleImg_SThresh, cmap='gray')
ax2.set_title('HLS S-Channel', fontsize=30)
interact(update,
min_thresh=(0,255),
max_thresh=(0,255))
print('...')
In [40]:
#HLS L-Channel
# Define a function that thresholds the L-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)
def hls_lthresh(img, thresh=(220, 255)):
# 1) Convert to HLS color space
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
hls_l = hls[:,:,1]
hls_l = hls_l*(255/np.max(hls_l))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(hls_l)
binary_output[(hls_l > thresh[0]) & (hls_l <= thresh[1])] = 1
# 3) Return a binary image of threshold result
return binary_output
print('...')
In [41]:
def update(min_thresh, max_thresh):
exampleImg_LThresh = hls_lthresh(exampleImg_unwarp, (min_thresh, max_thresh))
# Visualize hls l-channel threshold
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(exampleImg_unwarp)
ax1.set_title('Unwarped Image', fontsize=30)
ax2.imshow(exampleImg_LThresh, cmap='gray')
ax2.set_title('HLS L-Channel', fontsize=30)
interact(update,
min_thresh=(0,255),
max_thresh=(0,255))
print('...')
In [42]:
#LAB Colorspace
# Use exclusive lower bound (>) and inclusive upper (<=), OR the results of the thresholds (B channel should capture
# yellows)
def lab_bthresh(img, thresh=(190,255)):
# 1) Convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_RGB2Lab)
lab_b = lab[:,:,2]
# don't normalize if there are no yellows in the image
if np.max(lab_b) > 175:
lab_b = lab_b*(255/np.max(lab_b))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(lab_b)
binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 1
# 3) Return a binary image of threshold result
return binary_output
print('...')
In [43]:
def update(min_b_thresh, max_b_thresh):
exampleImg_LBThresh = lab_bthresh(exampleImg_unwarp, (min_b_thresh, max_b_thresh))
# Visualize LAB B threshold
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(exampleImg_unwarp)
ax1.set_title('Unwarped Image', fontsize=30)
ax2.imshow(exampleImg_LBThresh, cmap='gray')
ax2.set_title('LAB B-channel', fontsize=30)
interact(update,
min_b_thresh=(0,255),
max_b_thresh=(0,255))
print('...')
In [44]:
#Define Image Processing Pipeline
# Define the complete image processing pipeline, reads raw image and returns binary image with lane lines identified
# (hopefully)
def pipeline(img):
# Undistort
img_undistort = undistort(img)
# Perspective Transform
img_unwarp, M, Minv = unwarp(img_undistort, src, dst)
# Sobel Absolute (using default parameters)
#img_sobelAbs = abs_sobel_thresh(img_unwarp)
# Sobel Magnitude (using default parameters)
#img_sobelMag = mag_thresh(img_unwarp)
# Sobel Direction (using default parameters)
#img_sobelDir = dir_thresh(img_unwarp)
# HLS S-channel Threshold (using default parameters)
#img_SThresh = hls_sthresh(img_unwarp)
# HLS L-channel Threshold (using default parameters)
img_LThresh = hls_lthresh(img_unwarp)
# Lab B-channel Threshold (using default parameters)
img_BThresh = lab_bthresh(img_unwarp)
# Combine HLS and Lab B channel thresholds
combined = np.zeros_like(img_BThresh)
combined[(img_LThresh == 1) | (img_BThresh == 1)] = 1
return combined, Minv
print('...')
In [45]:
# Make a list of example images
images = glob.glob('./test_images/*.jpg')
# Set up plot
fig, axs = plt.subplots(len(images),2, figsize=(10, 20))
fig.subplots_adjust(hspace = .2, wspace=.001)
axs = axs.ravel()
i = 0
for image in images:
img = cv2.imread(image)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_bin, Minv = pipeline(img)
axs[i].imshow(img)
axs[i].axis('off')
i += 1
axs[i].imshow(img_bin, cmap='gray')
axs[i].axis('off')
i += 1
print('...')
In [46]:
#Sliding Window Polyfit
# Define method to fit polynomial to binary image with lines extracted, using sliding window
def sliding_window_polyfit(img):
# Take a histogram of the bottom half of the image
histogram = np.sum(img[img.shape[0]//2:,:], axis=0)
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
quarter_point = np.int(midpoint//2)
# Previously the left/right base was the max of the left/right half of the histogram
# this changes it so that only a quarter of the histogram (directly to the left/right) is considered
leftx_base = np.argmax(histogram[quarter_point:midpoint]) + quarter_point
rightx_base = np.argmax(histogram[midpoint:(midpoint+quarter_point)]) + midpoint
#print('base pts:', leftx_base, rightx_base)
# Choose the number of sliding windows
nwindows = 10
# Set height of windows
window_height = np.int(img.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = img.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 80
# Set minimum number of pixels found to recenter window
minpix = 40
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Rectangle data for visualization
rectangle_data = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = img.shape[0] - (window+1)*window_height
win_y_high = img.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
rectangle_data.append((win_y_low, win_y_high, win_xleft_low, win_xleft_high, win_xright_low, win_xright_high))
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit, right_fit = (None, None)
# Fit a second order polynomial to each
if len(leftx) != 0:
left_fit = np.polyfit(lefty, leftx, 2)
if len(rightx) != 0:
right_fit = np.polyfit(righty, rightx, 2)
visualization_data = (rectangle_data, histogram)
return left_fit, right_fit, left_lane_inds, right_lane_inds, visualization_data
print('...')
In [49]:
# visualize the result on example image
exampleImg = cv2.imread('./test_images/test5.jpg')
exampleImg = cv2.cvtColor(exampleImg, cv2.COLOR_BGR2RGB)
exampleImg_bin, Minv = pipeline(exampleImg)
left_fit, right_fit, left_lane_inds, right_lane_inds, visualization_data = sliding_window_polyfit(exampleImg_bin)
h = exampleImg.shape[0]
left_fit_x_int = left_fit[0]*h**2 + left_fit[1]*h + left_fit[2]
right_fit_x_int = right_fit[0]*h**2 + right_fit[1]*h + right_fit[2]
#print('fit x-intercepts:', left_fit_x_int, right_fit_x_int)
rectangles = visualization_data[0]
histogram = visualization_data[1]
# Create an output image to draw on and visualize the result
out_img = np.uint8(np.dstack((exampleImg_bin, exampleImg_bin, exampleImg_bin))*255)
# Generate x and y values for plotting
ploty = np.linspace(0, exampleImg_bin.shape[0]-1, exampleImg_bin.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
for rect in rectangles:
# Draw the windows on the visualization image
cv2.rectangle(out_img,(rect[2],rect[0]),(rect[3],rect[1]),(0,255,0), 2)
cv2.rectangle(out_img,(rect[4],rect[0]),(rect[5],rect[1]),(0,255,0), 2)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = exampleImg_bin.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [100, 200, 255]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
print('...')
In [50]:
# Print histogram from sliding window polyfit for example image
plt.plot(histogram)
plt.xlim(0, 1280)
print('...')
In [51]:
#Polyfit Using Fit from Previous Frame
# Define method to fit polynomial to binary image based upon a previous fit (chronologically speaking);
# this assumes that the fit will not change significantly from one video frame to the next
def polyfit_using_prev_fit(binary_warped, left_fit_prev, right_fit_prev):
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 80
left_lane_inds = ((nonzerox > (left_fit_prev[0]*(nonzeroy**2) + left_fit_prev[1]*nonzeroy + left_fit_prev[2] - margin)) &
(nonzerox < (left_fit_prev[0]*(nonzeroy**2) + left_fit_prev[1]*nonzeroy + left_fit_prev[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit_prev[0]*(nonzeroy**2) + right_fit_prev[1]*nonzeroy + right_fit_prev[2] - margin)) &
(nonzerox < (right_fit_prev[0]*(nonzeroy**2) + right_fit_prev[1]*nonzeroy + right_fit_prev[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit_new, right_fit_new = (None, None)
if len(leftx) != 0:
# Fit a second order polynomial to each
left_fit_new = np.polyfit(lefty, leftx, 2)
if len(rightx) != 0:
right_fit_new = np.polyfit(righty, rightx, 2)
return left_fit_new, right_fit_new, left_lane_inds, right_lane_inds
print('...')
In [55]:
# visualize the result on example image
exampleImg2 = cv2.imread('./test_images/test4.jpg')
exampleImg2 = cv2.cvtColor(exampleImg2, cv2.COLOR_BGR2RGB)
exampleImg2_bin, Minv = pipeline(exampleImg2)
margin = 80
left_fit2, right_fit2, left_lane_inds2, right_lane_inds2 = polyfit_using_prev_fit(exampleImg2_bin, left_fit, right_fit)
# Generate x and y values for plotting
ploty = np.linspace(0, exampleImg2_bin.shape[0]-1, exampleImg2_bin.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
left_fitx2 = left_fit2[0]*ploty**2 + left_fit2[1]*ploty + left_fit2[2]
right_fitx2 = right_fit2[0]*ploty**2 + right_fit2[1]*ploty + right_fit2[2]
# Create an image to draw on and an image to show the selection window
out_img = np.uint8(np.dstack((exampleImg2_bin, exampleImg2_bin, exampleImg2_bin))*255)
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
nonzero = exampleImg2_bin.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
out_img[nonzeroy[left_lane_inds2], nonzerox[left_lane_inds2]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds2], nonzerox[right_lane_inds2]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area (OLD FIT)
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx2, ploty, color='yellow')
plt.plot(right_fitx2, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
print('...')
In [56]:
#Radius of Curvature and Distance from Lane Center Calculation
# Method to determine radius of curvature and distance from lane center
# based on binary image, polynomial fit, and L and R lane pixel indices
def calc_curv_rad_and_center_dist(bin_img, l_fit, r_fit, l_lane_inds, r_lane_inds):
# Define conversions in x and y from pixels space to meters
ym_per_pix = 3.048/100 # meters per pixel in y dimension, lane line is 10 ft = 3.048 meters
xm_per_pix = 3.7/378 # meters per pixel in x dimension, lane width is 12 ft = 3.7 meters
left_curverad, right_curverad, center_dist = (0, 0, 0)
# Define y-value where we want radius of curvature
# I'll choose the maximum y-value, corresponding to the bottom of the image
h = bin_img.shape[0]
ploty = np.linspace(0, h-1, h)
y_eval = np.max(ploty)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = bin_img.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Again, extract left and right line pixel positions
leftx = nonzerox[l_lane_inds]
lefty = nonzeroy[l_lane_inds]
rightx = nonzerox[r_lane_inds]
righty = nonzeroy[r_lane_inds]
if len(leftx) != 0 and len(rightx) != 0:
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
# Distance from center is image x midpoint - mean of l_fit and r_fit intercepts
if r_fit is not None and l_fit is not None:
car_position = bin_img.shape[1]/2
l_fit_x_int = l_fit[0]*h**2 + l_fit[1]*h + l_fit[2]
r_fit_x_int = r_fit[0]*h**2 + r_fit[1]*h + r_fit[2]
lane_center_position = (r_fit_x_int + l_fit_x_int) /2
center_dist = (car_position - lane_center_position) * xm_per_pix
return left_curverad, right_curverad, center_dist
print('...')
In [57]:
rad_l, rad_r, d_center = calc_curv_rad_and_center_dist(exampleImg_bin, left_fit, right_fit, left_lane_inds, right_lane_inds)
print('Radius of curvature for example:', rad_l, 'm,', rad_r, 'm')
print('Distance from lane center for example:', d_center, 'm')
In [58]:
#Draw the Detected Lane Back onto the Original Image
def draw_lane(original_img, binary_img, l_fit, r_fit, Minv):
new_img = np.copy(original_img)
if l_fit is None or r_fit is None:
return original_img
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_img).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
h,w = binary_img.shape
ploty = np.linspace(0, h-1, num=h)# to cover same y-range as image
left_fitx = l_fit[0]*ploty**2 + l_fit[1]*ploty + l_fit[2]
right_fitx = r_fit[0]*ploty**2 + r_fit[1]*ploty + r_fit[2]
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
cv2.polylines(color_warp, np.int32([pts_left]), isClosed=False, color=(255,0,255), thickness=15)
cv2.polylines(color_warp, np.int32([pts_right]), isClosed=False, color=(0,255,255), thickness=15)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (w, h))
# Combine the result with the original image
result = cv2.addWeighted(new_img, 1, newwarp, 0.5, 0)
return result
print('...')
In [59]:
exampleImg_out1 = draw_lane(exampleImg, exampleImg_bin, left_fit, right_fit, Minv)
plt.imshow(exampleImg_out1)
print('...')
In [60]:
#Draw Curvature Radius and Distance from Center Data onto the Original Image
def draw_data(original_img, curv_rad, center_dist):
new_img = np.copy(original_img)
h = new_img.shape[0]
font = cv2.FONT_HERSHEY_DUPLEX
text = 'Curve radius: ' + '{:04.2f}'.format(curv_rad) + 'm'
cv2.putText(new_img, text, (40,70), font, 1.5, (200,255,155), 2, cv2.LINE_AA)
direction = ''
if center_dist > 0:
direction = 'right'
elif center_dist < 0:
direction = 'left'
abs_center_dist = abs(center_dist)
text = '{:04.3f}'.format(abs_center_dist) + 'm ' + direction + ' of center'
cv2.putText(new_img, text, (40,120), font, 1.5, (200,255,155), 2, cv2.LINE_AA)
return new_img
print('...')
In [61]:
exampleImg_out2 = draw_data(exampleImg_out1, (rad_l+rad_r)/2, d_center)
plt.imshow(exampleImg_out2)
print('...')
In [62]:
#Define a Line Class for Storing Data
# Define a class to receive the characteristics of each line detection
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = []
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#number of detected pixels
self.px_count = None
def add_fit(self, fit, inds):
# add a found fit to the line, up to n
if fit is not None:
if self.best_fit is not None:
# if we have a best fit, see how this new fit compares
self.diffs = abs(fit-self.best_fit)
if (self.diffs[0] > 0.001 or \
self.diffs[1] > 1.0 or \
self.diffs[2] > 100.) and \
len(self.current_fit) > 0:
# bad fit! abort! abort! ... well, unless there are no fits in the current_fit queue, then we'll take it
self.detected = False
else:
self.detected = True
self.px_count = np.count_nonzero(inds)
self.current_fit.append(fit)
if len(self.current_fit) > 5:
# throw out old fits, keep newest n
self.current_fit = self.current_fit[len(self.current_fit)-5:]
self.best_fit = np.average(self.current_fit, axis=0)
# or remove one from the history, if not found
else:
self.detected = False
if len(self.current_fit) > 0:
# throw out oldest fit
self.current_fit = self.current_fit[:len(self.current_fit)-1]
if len(self.current_fit) > 0:
# if there are still any fits in the queue, best_fit is their average
self.best_fit = np.average(self.current_fit, axis=0)
print('...')
In [64]:
#Define Complete Image Processing Pipeline
def process_image(img):
new_img = np.copy(img)
img_bin, Minv = pipeline(new_img)
# if both left and right lines were detected last frame, use polyfit_using_prev_fit, otherwise use sliding window
if not l_line.detected or not r_line.detected:
l_fit, r_fit, l_lane_inds, r_lane_inds, _ = sliding_window_polyfit(img_bin)
else:
l_fit, r_fit, l_lane_inds, r_lane_inds = polyfit_using_prev_fit(img_bin, l_line.best_fit, r_line.best_fit)
# invalidate both fits if the difference in their x-intercepts isn't around 350 px (+/- 100 px)
if l_fit is not None and r_fit is not None:
# calculate x-intercept (bottom of image, x=image_height) for fits
h = img.shape[0]
l_fit_x_int = l_fit[0]*h**2 + l_fit[1]*h + l_fit[2]
r_fit_x_int = r_fit[0]*h**2 + r_fit[1]*h + r_fit[2]
x_int_diff = abs(r_fit_x_int-l_fit_x_int)
if abs(350 - x_int_diff) > 100:
l_fit = None
r_fit = None
l_line.add_fit(l_fit, l_lane_inds)
r_line.add_fit(r_fit, r_lane_inds)
# draw the current best fit if it exists
if l_line.best_fit is not None and r_line.best_fit is not None:
img_out1 = draw_lane(new_img, img_bin, l_line.best_fit, r_line.best_fit, Minv)
rad_l, rad_r, d_center = calc_curv_rad_and_center_dist(img_bin, l_line.best_fit, r_line.best_fit,
l_lane_inds, r_lane_inds)
img_out = draw_data(img_out1, (rad_l+rad_r)/2, d_center)
else:
img_out = new_img
diagnostic_output = False
if diagnostic_output:
# put together multi-view output
diag_img = np.zeros((720,1280,3), dtype=np.uint8)
# original output (top left)
diag_img[0:360,0:640,:] = cv2.resize(img_out,(640,360))
# binary overhead view (top right)
img_bin = np.dstack((img_bin*255, img_bin*255, img_bin*255))
resized_img_bin = cv2.resize(img_bin,(640,360))
diag_img[0:360,640:1280, :] = resized_img_bin
# overhead with all fits added (bottom right)
img_bin_fit = np.copy(img_bin)
for i, fit in enumerate(l_line.current_fit):
img_bin_fit = plot_fit_onto_img(img_bin_fit, fit, (20*i+100,0,20*i+100))
for i, fit in enumerate(r_line.current_fit):
img_bin_fit = plot_fit_onto_img(img_bin_fit, fit, (0,20*i+100,20*i+100))
img_bin_fit = plot_fit_onto_img(img_bin_fit, l_line.best_fit, (255,255,0))
img_bin_fit = plot_fit_onto_img(img_bin_fit, r_line.best_fit, (255,255,0))
diag_img[360:720,640:1280,:] = cv2.resize(img_bin_fit,(640,360))
# diagnostic data (bottom left)
color_ok = (200,255,155)
color_bad = (255,155,155)
font = cv2.FONT_HERSHEY_DUPLEX
if l_fit is not None:
text = 'This fit L: ' + ' {:0.6f}'.format(l_fit[0]) + \
' {:0.6f}'.format(l_fit[1]) + \
' {:0.6f}'.format(l_fit[2])
else:
text = 'This fit L: None'
cv2.putText(diag_img, text, (40,380), font, .5, color_ok, 1, cv2.LINE_AA)
if r_fit is not None:
text = 'This fit R: ' + ' {:0.6f}'.format(r_fit[0]) + \
' {:0.6f}'.format(r_fit[1]) + \
' {:0.6f}'.format(r_fit[2])
else:
text = 'This fit R: None'
cv2.putText(diag_img, text, (40,400), font, .5, color_ok, 1, cv2.LINE_AA)
text = 'Best fit L: ' + ' {:0.6f}'.format(l_line.best_fit[0]) + \
' {:0.6f}'.format(l_line.best_fit[1]) + \
' {:0.6f}'.format(l_line.best_fit[2])
cv2.putText(diag_img, text, (40,440), font, .5, color_ok, 1, cv2.LINE_AA)
text = 'Best fit R: ' + ' {:0.6f}'.format(r_line.best_fit[0]) + \
' {:0.6f}'.format(r_line.best_fit[1]) + \
' {:0.6f}'.format(r_line.best_fit[2])
cv2.putText(diag_img, text, (40,460), font, .5, color_ok, 1, cv2.LINE_AA)
text = 'Diffs L: ' + ' {:0.6f}'.format(l_line.diffs[0]) + \
' {:0.6f}'.format(l_line.diffs[1]) + \
' {:0.6f}'.format(l_line.diffs[2])
if l_line.diffs[0] > 0.001 or \
l_line.diffs[1] > 1.0 or \
l_line.diffs[2] > 100.:
diffs_color = color_bad
else:
diffs_color = color_ok
cv2.putText(diag_img, text, (40,500), font, .5, diffs_color, 1, cv2.LINE_AA)
text = 'Diffs R: ' + ' {:0.6f}'.format(r_line.diffs[0]) + \
' {:0.6f}'.format(r_line.diffs[1]) + \
' {:0.6f}'.format(r_line.diffs[2])
if r_line.diffs[0] > 0.001 or \
r_line.diffs[1] > 1.0 or \
r_line.diffs[2] > 100.:
diffs_color = color_bad
else:
diffs_color = color_ok
cv2.putText(diag_img, text, (40,520), font, .5, diffs_color, 1, cv2.LINE_AA)
text = 'Good fit count L:' + str(len(l_line.current_fit))
cv2.putText(diag_img, text, (40,560), font, .5, color_ok, 1, cv2.LINE_AA)
text = 'Good fit count R:' + str(len(r_line.current_fit))
cv2.putText(diag_img, text, (40,580), font, .5, color_ok, 1, cv2.LINE_AA)
img_out = diag_img
return img_out
def plot_fit_onto_img(img, fit, plot_color):
if fit is None:
return img
new_img = np.copy(img)
h = new_img.shape[0]
ploty = np.linspace(0, h-1, h)
plotx = fit[0]*ploty**2 + fit[1]*ploty + fit[2]
pts = np.array([np.transpose(np.vstack([plotx, ploty]))])
cv2.polylines(new_img, np.int32([pts]), isClosed=False, color=plot_color, thickness=8)
return new_img
print('...')
Process Challenge Video
In [65]:
l_line = Line()
r_line = Line()
#my_clip.write_gif('test.gif', fps=12)
video_output1 = 'project_video_output.mp4'
video_input1 = VideoFileClip('project_video.mp4')#.subclip(22,26)
processed_video = video_input1.fl_image(process_image)
%time processed_video.write_videofile(video_output1, audio=False)
Process Challenge Video
In [67]:
l_line = Line()
r_line = Line()
video_output2 = 'challenge_video_output.mp4'
video_input2 = VideoFileClip('challenge_video.mp4')#.subclip(10,12)
#video_input2.save_frame("challenge01.jpeg", t=0.25) # saves the frame at time = 0.25s
processed_video = video_input2.fl_image(process_image)
%time processed_video.write_videofile(video_output2, audio=False)
Process Harder Challenge Video
In [ ]:
l_line = Line()
r_line = Line()
video_output3 = 'harder_challenge_video_output.mp4'
video_input3 = VideoFileClip('harder_challenge_video.mp4')#.subclip(0,3)
#video_input3.save_frame("hard_challenge01.jpeg") # saves the first frame
processed_video = video_input3.fl_image(process_image)
%time processed_video.write_videofile(video_output3, audio=False)
In [ ]: